Open for Reporting: An Exploration of Open Data and Journalism in Canada
Bibliographic record
Abstract
This thesis describes how open data and journalism have intersected within the Canadian context in a push for openness and transparency in government collected and produced data. Through a series of semi-structured interviews with Toronto-based data journalists, this thesis investigates how journalists use open data within the news production process, view themselves as open data advocates within the larger open data movement, and use data-driven journalism in an attempt to increase digital literacy and civic engagement within local communities. It will evaluate the challenges that journalists face in gathering government data through open data programs, and highlight the potential social and political pitfalls for the open data movement within Canada. The thesis concludes with policy recommendations to increase access to government held information and to promote the role of data journalism in a civic building capacity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.027 |
| Science and technology studies | 0.055 | 0.024 |
| Scholarly communication | 0.027 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".